AEs own the close, so I read outcome (bookings, win rate) against process health (coverage, velocity, hygiene). Watch for the 2025 trap: when most reps miss, the quota was set wrong more often than the reps failed.
Shape of it
7 metrics
Bookings against quota; also the share of reps hitting quota.
2025 team avg vs 100% plan
bookings / quotaBenchmark42.7% avg attainment (RepVue Q2 2025 / Ebsta × Pavilion); only 28% of reps hit 100% (down from 44% in 2022), and 76% missed in H1 2025. Report % of reps at 100%, not just the mean
The headline plan-health metric. When most miss, recalibrate quota-setting, do not just PIP reps.
Share of qualified opportunities won.
2025 median ~20%, top performers 30%+
won / (won + lost)Benchmark19–21% B2B in 2025, down from ~29% the prior year (Ebsta × Pavilion); top performers 30%+
Drives forecasting math and reveals qualification quality. Date-stamp it: win rates fell hard in 2025.
Mean annual contract value of won deals.
total ACV / deals wonFeeds capacity and quota models; shifts signal segment or pricing changes.
Median days from opportunity creation to close.
2025 avg 6.5mo, up from 4.9mo in 2019
Benchmark6.5 months avg B2B in 2025, up from 4.9 in 2019 (Ebsta); SMB under $15K runs 14–30 days, enterprise over $100K runs 90–180+ days. Use median, not mean
A lengthening cycle is an early warning on deal health and forecast risk.
Open pipeline relative to the target it must produce.
have 3.2x, need ~4x at a 25% win rate
open pipeline / quotaBenchmarkDerive it: coverage ≈ 1 / win rate. A 20% win rate implies 5x, not the default 3x
The fastest read on reachability. A flat 3x understates true need when win rates fall.
The speed at which pipeline converts to revenue.
(# opps × win rate × avg deal size) / cycle lengthA single compound metric that captures the health of the whole engine.
Share of committed deals that push to a later period.
inspect commits above ~15%
committed deals that slipped / total committedHigh slippage erodes forecast credibility and signals soft commits.